MolmoAct is an innovative Action Reasoning Model (ARM) developed to enhance spatial reasoning in robotics, allowing machines to understand and execute tasks in three-dimensional space. Built on the open-source Molmo framework, MolmoAct utilizes depth-aware perception tokens for improved action planning and execution, demonstrating superior performance and generalization capabilities in real-world scenarios. The model is fully open-source, promoting transparency and accessibility for further research and development in the field.

TimeScope is an open-source benchmark that evaluates the understanding of long videos by vision-language models through localized retrieval, information synthesis, and fine-grained temporal perception. By inserting short video clips into longer videos, it challenges models to demonstrate true temporal comprehension rather than surface-level recognition, revealing that many state-of-the-art models struggle with these tasks. The benchmark aims to drive improvements in how multimodal systems are trained and evaluated.

Kimi-VL is an open-source Mixture-of-Experts vision-language model that excels in multimodal reasoning and long-context understanding with only 2.8B activated parameters. It demonstrates superior performance in various tasks such as multi-turn interactions, video comprehension, and mathematical reasoning, competing effectively with larger models while maintaining efficiency. The latest variant, Kimi-VL-A3B-Thinking-2506, enhances reasoning and visual perception capabilities, achieving state-of-the-art results in several benchmarks.

SmolVLA is a compact and open-source Vision-Language-Action model designed for robotics, capable of running on consumer hardware and trained on community-shared datasets. It significantly outperforms larger models in both simulation and real-world tasks, while offering faster response times through asynchronous inference. The model's lightweight architecture and efficient training methods aim to democratize access to advanced robotics capabilities.